How long code takes in seconds depends on the computer. What matters more is how the work grows when the input gets bigger. Programmers describe that with Big-O notation:

Name	Big-O	Example	1,000 items	1,000,000 items
constant	O(1)	looking up `list[i]`, `dict[key]`	1 step	1 step
logarithmic	O(log n)	binary search	~10	~20
linear	O(n)	linear search, `sum`, one loop	1,000	1,000,000
n log n	O(n log n)	good sorting (next unit)	~10,000	~20,000,000
quadratic	O(n²)	a loop inside a loop over the same list	1,000,000	1,000,000,000,000

Big-O ignores the details and keeps the shape: whether doubling the input doubles the work, quadruples it, or barely changes it. A quadratic algorithm that's fine for 100 items can be hopeless for a million.